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Champion: Andriy Nikolov
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Participant(s):Enrico Motta

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KnoFuss

Information fusion system for semantic datasets

KnoFuss represents a modular architecture for automated knowledge fusion of Semantic Web repositories. Knowledge fusion involves two main subtasks: coreference resolution and inconsistency resolution. Different datasets often contain information about the same entities but refer to them using different URIs. Identifying such individuals is the task of coreference resolution. Moreover, pieces of data defined in different knowledge bases can influence each other (e.g., they can be mutually contradictory). Inconsistency resolution is needed to process such cases. The KnoFuss architecture allows different methods for coreference resolution and inconsistency resolution to be combined in order to improve the quality of resulting data.

News

15 Jun 2016

Mathieu d'Aquin


Publications

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Nikolov, A., d'Aquin, M. and Motta, E. (2012) Unsupervised Learning of Link Discovery Configuration, 9th Extended Semantic Web Conference (ESWC 2012), Heraklion, Greece

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Nikolov, A. and Motta, E. (2010) Capturing emerging relations between schema ontologies on the Web of Data, Workshop: Workshop on Consuming Linked Data at 9th International Semantic Web Conference, Shanghai, China

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Nikolov, A., Uren, V. and Motta, E. (2010) Data Linking: Capturing and Utilising Implicit Schema-level Relations, Workshop: Linked Data on the Web (LDOW 2010) at 19th International World Wide Web Conference (WWW 2010), Raleigh, USA

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Nikolov, A., Uren, V., Motta, E. and Roeck, A. (2009) Overcoming Schema Heterogeneity between Linked Semantic Repositories to Improve Coreference Resolution, 4th Asian Semantic Web Conference (ASWC 2009), Shanghai, China

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Nikolov, A., Uren, V., Motta, E. and Roeck, A. (2009) Towards data fusion in a multi-ontology environment, Workshop: Linked Data on the Web at 18th International World Wide Web Conference (WWW 2009), Madrid, Spain

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Latest Seminar
Microsoft Research Cambridge

Actions and their Consequences: Implicit Interactions with Machine Learned Knowledge Bases

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